AI security refers to the use of artificial intelligence (AI) in enhancing an organization’s security posture. When used correctly, AI can automate the processes of detecting threats as well as preventing and remediating data breaches and malware, ransomware, and other cyberattacks.

AI-powered tools also help to respond to cyberthreats quickly, which can narrow the attack surface and minimize damage. 

AI security can also refer to tools and techniques used to defend AI systems and infrastructure from attacks. This article focuses on the former - the use of AI in defending computer networks and systems against malware and ransomware attacks.

Malware and ransomware are the two most prevalent cyberthreat vectors organizations face that AI-powered security tools can defend computer networks against. Malware is ‘malicious software’ designed by cybercriminals to steal data or destroy computer infrastructure and systems. 

On the other hand, ransomware is a type of malware that blocks access to a computer system until a sum of money is paid. Ransomware is particularly costly and disruptive for businesses. According to Veeam’s Global Ransomware Trends Report for 2024, 33% of organizations that paid ransomware could not recover their data.

How to use AI to defend company networks against the main cyberthreat vectors

Phishing attacks

Phishing is a form of fraud where a cybercriminal sends an email or other type of message pretending to be representing a reputable company to trick individuals into revealing sensitive or private information. You know those emails that look like they're from your bank but are actually just trying to steal your information? 

Phishing attacks are becoming more sophisticated and harder to stop than ever before. However, AI can help. You use it to analyze patterns in emails and detect the subtle signs of phishing. 

For example, AI can spot anomalies in the sender's email address or the urgency in the language of an email. If an email usually comes from john.doe@yourbank.com and suddenly you get one from john.doe@yourb4nk.com, AI will catch that. It might also notice if an email is urgently asking you to click a link or provide personal information, which is a common phishing tactic.

It's not just about the text either. AI looks at the metadata of emails too. Things like the originating IP address, the server it came from, and even how the email is formatted. It’s amazing how much data there is behind what looks like a simple email. By analyzing all this, AI can flag suspicious emails before you even open them.

Then there’s the real-time learning aspect. AI systems learn from every phishing email they detect. Because of this, they get better over time. In fact, AI every phishing attempt can make the next one less likely to succeed. AI turns a reactive process into a proactive defense mechanism.

You can also integrate AI with other tools. For example, when an email is flagged as suspicious, it can automatically trigger additional security measures. 

Maybe it prompts a two-factor authentication if you try to click on the link in the email. Or it quarantines the email and notifies the IT team. This way, AI doesn’t just detect threats but helps manage the response too.

Importantly, AI can help educate us as well. Many systems offer insights on why an email was flagged. This can train employees to recognize phishing attempts themselves. Over time, the whole team becomes more vigilant, reducing the chances of a successful attack even further.

DDoS attacks

DDoS (Distributed Denial of Service) attacks flood your website or its key resources with fake traffic to make it impossible for your customers to get through. It sounds like something out of a sci-fi movie, but these attacks are as real as it gets and can paralyze a company. 

DDoS attacks are hard to contain once they breach your network defenses. AI, however, can stop them from happening in the first place. 

For example, an AI system can identify abnormal traffic patterns way faster than a human can. If your usual website traffic is 100 requests per second and suddenly it spikes to 10,000, an AI system can flag it immediately. 

AI systems can analyze traffic data in real-time. They can differentiate between legitimate spikes—like Black Friday sales—and malicious attempts to crash your server. Some systems can even use machine learning to adapt to new types of attacks as they happen. The AI gets smarter with each attempt, making it increasingly difficult for attackers to break through.

In practical terms, AI can automatically reroute bad traffic away from your main server. There are AI algorithms that can divert harmful traffic to a "honeypot" server that traps the malicious traffic while your real server stays up and running. 

AI security experts are now even developing AI systems that can predict potential DDoS attacks before they happen. They fed their system heaps of historical data from past attacks, making it capable of forecasting future threats with impressive accuracy.

The beauty of AI in this context is its speed and efficiency. Traditional methods might involve manually sifting through logs or setting up static rules. But AI evolves with the threat landscape. 

For instance, AI systems can now detect and mitigate "low and slow" attacks that aim to fly under the radar. These attacks send traffic at a low rate to avoid detection, but AI can recognize and disrupt these tactics effectively.

So, combining AI with cybersecurity means developing a dynamic defense mechanism. It’s not just about stopping an attack; it’s about anticipating and adapting to future ones. 

Insider threats

Insider threats extend beyond rogue employees. In fact, they often come from negligence or unintentional actions. For example, an employee who clicks on a phishing email. They might not intend to harm the company, but they've just opened a door for attackers.

AI can help you spot these unintentional insider threats. Machine learning algorithms can analyze user behavior patterns. If someone starts downloading massive amounts of data, AI can flag that.

Then there's the issue of privileged access. Not everyone needs access to your sensitive IT systems. AI systems can manage access controls dynamically. 

If an employee suddenly needs access to a restricted area, AI can assess the request in real-time. Before granting access, AI considers the context: 

• Is this normal for their role? 
• Have they accessed this data before? 

This helps in making quick, informed decisions.

You must also consider insider threats from former employees. Once someone leaves the company, their access should be revoked immediately. AI can automate this process, ensuring there are no gaps. If the system notices that an access revocation didn't go through, it can alert the IT team immediately.

AI can even help in monitoring communications. Natural Language Processing (NLP) algorithms can scan emails and messages for sensitive information leaks. Let's say an employee is about to send a file containing trade secrets. The AI can flag this and either block the email or alert the security team.

Another important aspect of AI in the context of insider threats is training. AI can personalize security training programs based on employee behavior. If the system notices someone frequently clicking on suspicious links, it can recommend specific training modules for them. It's about being proactive rather than reactive.

Therefore, incorporating AI into your security measures allows you to stay one step ahead of potential insider threats. By analyzing patterns and behaviors, it can help you protect your network more effectively.

AI use cases and applications in cybersecurity

Threat detection and prevention

One of the most compelling aspects of AI’s use in network security is its ability to detect anomalies faster than traditional methods. For instance, if an employee inadvertently downloads a malicious file, AI can spot this anomaly and flag it before it wreaks havoc.

Let’s illustrate with an example. Your network gets bombarded with countless data packets every minute. Manually sifting through this data for potential threats would be nearly impossible. AI, however, can analyze these huge volumes of data in real-time. It can identify patterns that indicate a potential security breach. 

For example, if an employee's account suddenly tries to access sensitive files at odd hours, the AI system can immediately recognize this unusual behavior and lock the account, thereby preventing possible data theft.

AI security tools can also predict potential threats. By analyzing past attack patterns, AI can forecast future vulnerabilities. For example, knowing that a certain type of phishing email is likely to target your company next month. With this foresight, you can educate your team, update your filters, and be prepared in advance.

AI can adapt and learn over time. Think about it like training a dog to fetch. The more you train, the better it gets. Similarly, AI algorithms get smarter with each threat they analyze. If a new type of malware emerges, AI can learn from it and instantly apply this new knowledge to protect the network. This adaptability is crucial because cyber threats are constantly evolving.

You can also leverage AI for automated responses. Say an intrusion is detected; the system can automatically isolate the affected segment of the network. This minimizes the damage and gives your IT team time to assess and resolve the issue without the threat spreading.

Vulnerability management

The interconnectedness of today’s digital world means bad actors are continually evolving their methods to exploit system vulnerabilities. This makes effective vulnerability management a must for any organization looking to protect itself from breaches. AI can help in several ways. 

Automated vulnerability scanning

One of its primary applications in vulnerability management is automated vulnerability scanning. Traditional scanners rely on predefined signatures and patterns to detect known vulnerabilities. These are helpful but often can't keep up with the rapidly changing threat landscape. 

By automating the scanning process, AI reduces the need for manual intervention. This means organizations can conduct more frequent and comprehensive vulnerability assessments, strengthening their overall security.

Data analysis

AI, on the other hand, uses machine learning algorithms to analyze massive amounts of data. It proactively identifies potential vulnerabilities by learning from new threats and attack patterns. This capability means AI-powered scanners can find vulnerabilities that traditional tools might miss.

AI-driven scanners also adapt to dynamic environments and evolving threats. They detect anomalies and deviations from expected behavior, flagging potential vulnerabilities before attackers can exploit them. 

Risk scoring

Once vulnerabilities are detected, organizations need to assess the risks they pose and prioritize them. AI is excellent at this. Algorithms analyze various factors such as the nature of the vulnerability, the system’s importance, and potential exploitation impacts. 

The AI algorithms will then automatically assign a risk score to each vulnerability. By considering multiple data points and historical attack data, AI provides a more accurate risk assessment.

AI-powered risk assessment tools can also offer insights into how vulnerabilities might impact business operations. This aids organizations in making informed decisions about resource allocation and risk mitigation. By incorporating risk assessment into vulnerability management processes, organizations ensure they prioritize their efforts effectively and allocate resources where they’re needed most.

Continuous monitoring

AI-powered threat intelligence platforms continuously monitor emerging threats and vulnerabilities. This keeps organizations ahead of potential exploits. By integrating threat intelligence feeds with vulnerability management systems, organizations can proactively identify and mitigate risks before attackers get a chance.

AI can also enhance intrusion detection and prevention systems by analyzing network traffic patterns. It identifies suspicious behavior and responds to potential threats in real-time. This minimizes the impact of security incidents and helps prevent data breaches.

Predictive analysis

The ability to foresee potential threats before they even happen is a network security superpower. As hard as it may seem to achieve it, it is what predictive analysis gives you. 

Trend spotting

By leveraging advanced algorithms and machine learning, you can analyze vast amounts of data in real-time. This means spotting anomalies and suspicious activities that might go unnoticed by traditional systems.

Let's say a hacker tries to infiltrate your network by mimicking the behavior of a legitimate user. Predictive analysis can help you detect subtle differences in login patterns, access times, and data usage that don't fit the usual profile of the genuine user.

Malware detection

Traditional antivirus programs rely on known signatures to spot threats, but they are woefully inadequate when used for spotting new, unknown malware. 

Predictive analysis uses behavioral cues and predictive models to flag potentially harmful software before it gets a chance to wreak havoc. Remember the WannaCry ransomware attack? With predictive analysis, similar threats can be identified early by recognizing unusual file encryption activities.

Resource optimization

Predictive analysis helps to optimize your security resources. Instead of a blanket approach where every alert is treated equally, the system prioritizes threats based on their likelihood and potential impact. This ensures your IT team can focus on the most pressing issues first. 

For instance, if a certain IP address is found probing your network repeatedly, predictive algorithms can assess the risk level and alert you if it's likely a prelude to a larger attack.

So, by integrating predictive analysis into our AI security measures, we’re not just reacting to threats; we’re staying a step ahead. This proactive stance translates to a more secure and resilient network, safeguarding our company's precious data and keeping our operations running smoothly.

Patch management

Patch management entails keeping all your software up-to-date to protect against vulnerabilities. Think of it as regular maintenance, like changing the oil in your car. It ensures everything runs smoothly and securely.

So, how does AI fit into patch management? 

Automates patch identification and application

AI can automate the process of identifying and applying patches. AI-powered cybersecurity tools can constantly scan your entire network for outdated software or known vulnerabilities. It can then automatically download and apply the necessary patches. This not only saves time but also reduces the risk of human error, which is a big deal.

Let's say there's a known vulnerability in a popular web server software your company uses. An AI system can identify this vulnerability as soon as it's made public. It would then alert you and apply the patch, often faster than a human could. This immediate action helps protect your company from potential attacks that exploit the vulnerability.

Patch prioritization

AI can prioritize which patches are most critical. Not every update is urgent. Some might address minor issues, while others fix serious security flaws. AI can analyze the severity of each vulnerability and decide which patches need to be applied first. This way, you focus your resources on the most pressing issues.

An example is where multiple patches are released simultaneously for different software. Your AI system can quickly assess each one, prioritize them, and patch the most critical vulnerabilities first. It is a relief knowing you are protected without having to manually sift through each update.

Streamlines patch rollbacks

AI can help manage patch rollbacks. Sometimes, a patch might cause unexpected issues. AI can monitor for these problems and, if necessary, roll back the patch to the previous stable version. This minimizes downtime and keeps the network running smoothly.

In practice, integrating AI into patch management can involve using tools like IBM's Watson or Microsoft's Azure Security Center. These platforms offer AI-driven solutions to automate and enhance patch management processes. They continuously learn from new data, improving their effectiveness over time.

So, patch management with AI isn't just about efficiency; it's about staying ahead of potential threats. AI helps ensure that your networks are always protected with the latest security updates, giving you peace of mind and more time to focus on other important tasks.

Compliance and reporting for AI security tools

Ensuring compliance in AI security requires clear policies and procedures. First, you need to establish a comprehensive compliance program to monitor your AI systems. 

This entails but extends well beyond regularly auditing your AI applications to ensure they meet legal and ethical standards. For example, if you're using AI for data analysis, you must ensure the data is collected and processed legally.

Establish an AI governance framework 

Your AI governance framework should outline who is responsible for AI compliance within your organization. For instance, appointing a dedicated AI compliance officer can help maintain oversight and accountability. This person would be responsible for regularly reviewing AI systems to ensure they do not discriminate against any group or invade privacy.

Data privacy and security are critical. You must safeguard personal data handled by your AI systems. Suppose you are using an AI-powered photo editing app. You must ensure that this app doesn't mishandle user data, similar to the issues faced by FaceApp. Ensuring that all data processing activities comply with regulations like GDPR will protect your organization from hefty fines.

Train your employees on ethical AI use and compliance

Training your personnel on AI compliance requirements is essential. Everyone from developers to managers should understand the importance of adhering to these regulations. Providing regular training sessions and updates on the latest compliance standards will keep everyone informed.

Automated tools can be incredibly helpful. These tools can monitor AI compliance in real-time, flagging potential issues before they become significant problems. For example, employing software that tracks and reports on AI decision-making processes can help identify biases early.

Finally, establishing a robust reporting process is vital. If compliance issues arise, having a clear protocol for addressing these problems will streamline our response. This might involve creating a dedicated reporting channel where employees can report any concerns related to AI compliance.

By adopting these practices, you can ensure your AI systems are secure and compliant, safeguarding both your organization and the individuals impacted by your AI applications.